Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = '/input'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x7f4c70d20e80>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x7f4c70ff4940>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, shape=(None, image_height, image_width, image_channels))
    input_z = tf.placeholder(tf.float32, shape=(None, z_dim))
    learning_rate = tf.placeholder(tf.float32, shape=())

    return input_real, input_z, learning_rate

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x32
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [10]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 2*2*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 2, 2, 512))
        # x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 4x4x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 16x16x256 now
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        # 32x32x3 now
        
        #logits = tf.slice(logits, [0, 2, 2, 0], [-1, 28, 28, -1])
        out = tf.tanh(logits)
        
        return out
    
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [11]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [12]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [13]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [15]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    n_samples, width, height, channels = data_shape
    input_real, input_z, learn_rate = model_inputs(width, height, channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    show_every = 50
    print_every = 10
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                batch_images *= 2
                # TODO: Train Model
                steps += 1
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})

                if steps % show_every == 0:
                    n_images = 16
                    show_generator_output(sess, n_images, input_z, channels, data_image_mode)

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))               
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [16]:
batch_size = 64
z_dim = 100
learning_rate = 0.002
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 0/2... Discriminator Loss: 8.5140... Generator Loss: 0.0003
Epoch 0/2... Discriminator Loss: 3.6108... Generator Loss: 0.0668
Epoch 0/2... Discriminator Loss: 2.4611... Generator Loss: 5.6333
Epoch 0/2... Discriminator Loss: 0.8260... Generator Loss: 3.0077
Epoch 0/2... Discriminator Loss: 1.1195... Generator Loss: 1.6994
Epoch 0/2... Discriminator Loss: 0.5629... Generator Loss: 4.0761
Epoch 0/2... Discriminator Loss: 2.0052... Generator Loss: 5.6132
Epoch 0/2... Discriminator Loss: 0.6540... Generator Loss: 3.6345
Epoch 0/2... Discriminator Loss: 2.0375... Generator Loss: 3.1174
Epoch 0/2... Discriminator Loss: 1.2745... Generator Loss: 2.5982
Epoch 0/2... Discriminator Loss: 1.1530... Generator Loss: 3.5352
Epoch 0/2... Discriminator Loss: 1.1068... Generator Loss: 1.5601
Epoch 0/2... Discriminator Loss: 1.4640... Generator Loss: 3.1787
Epoch 0/2... Discriminator Loss: 1.7672... Generator Loss: 2.7006
Epoch 0/2... Discriminator Loss: 1.2874... Generator Loss: 1.6922
Epoch 0/2... Discriminator Loss: 1.7234... Generator Loss: 1.3984
Epoch 0/2... Discriminator Loss: 1.3289... Generator Loss: 0.9357
Epoch 0/2... Discriminator Loss: 1.0086... Generator Loss: 1.0691
Epoch 0/2... Discriminator Loss: 1.2930... Generator Loss: 1.0155
Epoch 0/2... Discriminator Loss: 1.7270... Generator Loss: 2.4769
Epoch 0/2... Discriminator Loss: 1.4020... Generator Loss: 2.1710
Epoch 0/2... Discriminator Loss: 1.2873... Generator Loss: 1.5074
Epoch 0/2... Discriminator Loss: 1.3220... Generator Loss: 1.5206
Epoch 0/2... Discriminator Loss: 1.4258... Generator Loss: 2.1655
Epoch 0/2... Discriminator Loss: 1.3759... Generator Loss: 1.7663
Epoch 0/2... Discriminator Loss: 1.6318... Generator Loss: 1.9183
Epoch 0/2... Discriminator Loss: 1.3130... Generator Loss: 1.1327
Epoch 0/2... Discriminator Loss: 1.5549... Generator Loss: 0.9682
Epoch 0/2... Discriminator Loss: 1.6721... Generator Loss: 0.3074
Epoch 0/2... Discriminator Loss: 1.3366... Generator Loss: 0.4284
Epoch 0/2... Discriminator Loss: 1.5074... Generator Loss: 0.3967
Epoch 0/2... Discriminator Loss: 1.5547... Generator Loss: 0.3455
Epoch 0/2... Discriminator Loss: 1.2647... Generator Loss: 0.5051
Epoch 0/2... Discriminator Loss: 1.4682... Generator Loss: 0.4720
Epoch 0/2... Discriminator Loss: 1.4950... Generator Loss: 0.3524
Epoch 0/2... Discriminator Loss: 1.4338... Generator Loss: 0.4322
Epoch 0/2... Discriminator Loss: 1.5909... Generator Loss: 0.4239
Epoch 0/2... Discriminator Loss: 1.6115... Generator Loss: 0.2961
Epoch 0/2... Discriminator Loss: 1.4366... Generator Loss: 1.7627
Epoch 0/2... Discriminator Loss: 1.3722... Generator Loss: 1.7704
Epoch 0/2... Discriminator Loss: 1.3806... Generator Loss: 1.6024
Epoch 0/2... Discriminator Loss: 1.2323... Generator Loss: 1.4415
Epoch 0/2... Discriminator Loss: 1.2894... Generator Loss: 1.6225
Epoch 0/2... Discriminator Loss: 0.8799... Generator Loss: 1.0099
Epoch 0/2... Discriminator Loss: 1.2763... Generator Loss: 1.3832
Epoch 0/2... Discriminator Loss: 1.2470... Generator Loss: 1.0047
Epoch 0/2... Discriminator Loss: 1.2416... Generator Loss: 1.1033
Epoch 0/2... Discriminator Loss: 1.4812... Generator Loss: 1.1818
Epoch 0/2... Discriminator Loss: 1.2616... Generator Loss: 1.6416
Epoch 0/2... Discriminator Loss: 1.0612... Generator Loss: 1.3681
Epoch 0/2... Discriminator Loss: 1.3914... Generator Loss: 1.1762
Epoch 0/2... Discriminator Loss: 1.1906... Generator Loss: 1.1167
Epoch 0/2... Discriminator Loss: 1.3248... Generator Loss: 1.0954
Epoch 0/2... Discriminator Loss: 1.1873... Generator Loss: 1.3103
Epoch 0/2... Discriminator Loss: 1.4150... Generator Loss: 1.3057
Epoch 0/2... Discriminator Loss: 1.7785... Generator Loss: 2.1457
Epoch 0/2... Discriminator Loss: 1.4263... Generator Loss: 1.1965
Epoch 0/2... Discriminator Loss: 1.2120... Generator Loss: 1.2874
Epoch 0/2... Discriminator Loss: 1.1136... Generator Loss: 1.1684
Epoch 0/2... Discriminator Loss: 1.2929... Generator Loss: 0.7753
Epoch 0/2... Discriminator Loss: 1.5971... Generator Loss: 0.3194
Epoch 0/2... Discriminator Loss: 1.3856... Generator Loss: 0.4429
Epoch 0/2... Discriminator Loss: 1.5272... Generator Loss: 0.3839
Epoch 0/2... Discriminator Loss: 1.3213... Generator Loss: 0.4256
Epoch 0/2... Discriminator Loss: 1.4313... Generator Loss: 0.3856
Epoch 0/2... Discriminator Loss: 1.4622... Generator Loss: 0.3965
Epoch 0/2... Discriminator Loss: 1.6465... Generator Loss: 0.3191
Epoch 0/2... Discriminator Loss: 1.4733... Generator Loss: 0.3725
Epoch 0/2... Discriminator Loss: 1.6427... Generator Loss: 0.3054
Epoch 0/2... Discriminator Loss: 1.2546... Generator Loss: 0.5687
Epoch 0/2... Discriminator Loss: 1.7889... Generator Loss: 0.2451
Epoch 0/2... Discriminator Loss: 1.4912... Generator Loss: 0.3826
Epoch 0/2... Discriminator Loss: 1.2433... Generator Loss: 0.5989
Epoch 0/2... Discriminator Loss: 1.6034... Generator Loss: 0.3276
Epoch 0/2... Discriminator Loss: 1.2604... Generator Loss: 0.5116
Epoch 0/2... Discriminator Loss: 1.3981... Generator Loss: 0.4928
Epoch 0/2... Discriminator Loss: 1.3570... Generator Loss: 0.4385
Epoch 0/2... Discriminator Loss: 1.3702... Generator Loss: 0.4244
Epoch 0/2... Discriminator Loss: 1.3792... Generator Loss: 0.4676
Epoch 0/2... Discriminator Loss: 1.4955... Generator Loss: 0.3408
Epoch 0/2... Discriminator Loss: 1.5549... Generator Loss: 0.3743
Epoch 0/2... Discriminator Loss: 1.4994... Generator Loss: 0.3801
Epoch 0/2... Discriminator Loss: 1.4831... Generator Loss: 0.4253
Epoch 0/2... Discriminator Loss: 1.2933... Generator Loss: 0.6187
Epoch 0/2... Discriminator Loss: 1.3476... Generator Loss: 0.4816
Epoch 0/2... Discriminator Loss: 1.3419... Generator Loss: 0.4596
Epoch 0/2... Discriminator Loss: 1.2186... Generator Loss: 0.6869
Epoch 0/2... Discriminator Loss: 1.4868... Generator Loss: 0.4188
Epoch 0/2... Discriminator Loss: 1.3549... Generator Loss: 0.4081
Epoch 0/2... Discriminator Loss: 1.2668... Generator Loss: 0.4785
Epoch 0/2... Discriminator Loss: 1.3360... Generator Loss: 1.2991
Epoch 0/2... Discriminator Loss: 1.2267... Generator Loss: 1.1430
Epoch 0/2... Discriminator Loss: 1.5437... Generator Loss: 1.7955
Epoch 1/2... Discriminator Loss: 1.1664... Generator Loss: 0.9010
Epoch 1/2... Discriminator Loss: 1.5408... Generator Loss: 0.3276
Epoch 1/2... Discriminator Loss: 1.4801... Generator Loss: 0.3421
Epoch 1/2... Discriminator Loss: 1.3202... Generator Loss: 0.4227
Epoch 1/2... Discriminator Loss: 1.3967... Generator Loss: 0.4876
Epoch 1/2... Discriminator Loss: 1.0960... Generator Loss: 0.6073
Epoch 1/2... Discriminator Loss: 1.7487... Generator Loss: 0.2895
Epoch 1/2... Discriminator Loss: 1.8997... Generator Loss: 0.2385
Epoch 1/2... Discriminator Loss: 1.0213... Generator Loss: 1.0772
Epoch 1/2... Discriminator Loss: 1.2334... Generator Loss: 0.8377
Epoch 1/2... Discriminator Loss: 1.2667... Generator Loss: 1.4338
Epoch 1/2... Discriminator Loss: 2.2859... Generator Loss: 2.6367
Epoch 1/2... Discriminator Loss: 1.2521... Generator Loss: 0.7799
Epoch 1/2... Discriminator Loss: 1.1867... Generator Loss: 1.5075
Epoch 1/2... Discriminator Loss: 1.1154... Generator Loss: 1.4028
Epoch 1/2... Discriminator Loss: 1.2366... Generator Loss: 1.5350
Epoch 1/2... Discriminator Loss: 1.0955... Generator Loss: 1.5038
Epoch 1/2... Discriminator Loss: 1.2613... Generator Loss: 0.6311
Epoch 1/2... Discriminator Loss: 1.0120... Generator Loss: 0.8886
Epoch 1/2... Discriminator Loss: 1.1753... Generator Loss: 1.1975
Epoch 1/2... Discriminator Loss: 1.0208... Generator Loss: 1.1760
Epoch 1/2... Discriminator Loss: 1.2030... Generator Loss: 1.4197
Epoch 1/2... Discriminator Loss: 1.4204... Generator Loss: 1.5995
Epoch 1/2... Discriminator Loss: 1.1857... Generator Loss: 0.7495
Epoch 1/2... Discriminator Loss: 1.1838... Generator Loss: 1.8811
Epoch 1/2... Discriminator Loss: 0.8887... Generator Loss: 1.1582
Epoch 1/2... Discriminator Loss: 1.3022... Generator Loss: 1.1383
Epoch 1/2... Discriminator Loss: 1.0182... Generator Loss: 0.8072
Epoch 1/2... Discriminator Loss: 1.0032... Generator Loss: 0.8647
Epoch 1/2... Discriminator Loss: 1.7389... Generator Loss: 0.2961
Epoch 1/2... Discriminator Loss: 0.8422... Generator Loss: 2.1284
Epoch 1/2... Discriminator Loss: 1.3447... Generator Loss: 0.4614
Epoch 1/2... Discriminator Loss: 1.4934... Generator Loss: 0.3605
Epoch 1/2... Discriminator Loss: 1.5602... Generator Loss: 0.3510
Epoch 1/2... Discriminator Loss: 1.2655... Generator Loss: 0.4246
Epoch 1/2... Discriminator Loss: 1.2483... Generator Loss: 0.4864
Epoch 1/2... Discriminator Loss: 2.0382... Generator Loss: 0.1852
Epoch 1/2... Discriminator Loss: 1.4371... Generator Loss: 0.3991
Epoch 1/2... Discriminator Loss: 1.6131... Generator Loss: 0.3284
Epoch 1/2... Discriminator Loss: 0.8679... Generator Loss: 1.0999
Epoch 1/2... Discriminator Loss: 0.9064... Generator Loss: 1.1679
Epoch 1/2... Discriminator Loss: 1.5586... Generator Loss: 2.8438
Epoch 1/2... Discriminator Loss: 1.4894... Generator Loss: 0.3550
Epoch 1/2... Discriminator Loss: 1.4370... Generator Loss: 0.4299
Epoch 1/2... Discriminator Loss: 2.2213... Generator Loss: 2.9856
Epoch 1/2... Discriminator Loss: 1.1252... Generator Loss: 0.5912
Epoch 1/2... Discriminator Loss: 1.4690... Generator Loss: 0.3774
Epoch 1/2... Discriminator Loss: 1.1495... Generator Loss: 0.7052
Epoch 1/2... Discriminator Loss: 1.1139... Generator Loss: 0.9397
Epoch 1/2... Discriminator Loss: 0.9943... Generator Loss: 1.3911
Epoch 1/2... Discriminator Loss: 1.1960... Generator Loss: 1.6696
Epoch 1/2... Discriminator Loss: 0.6920... Generator Loss: 1.2504
Epoch 1/2... Discriminator Loss: 1.2002... Generator Loss: 1.8912
Epoch 1/2... Discriminator Loss: 1.4406... Generator Loss: 2.5291
Epoch 1/2... Discriminator Loss: 0.7440... Generator Loss: 1.0393
Epoch 1/2... Discriminator Loss: 1.5926... Generator Loss: 2.2092
Epoch 1/2... Discriminator Loss: 0.5891... Generator Loss: 1.1394
Epoch 1/2... Discriminator Loss: 0.4935... Generator Loss: 1.2681
Epoch 1/2... Discriminator Loss: 1.1554... Generator Loss: 0.5547
Epoch 1/2... Discriminator Loss: 0.5235... Generator Loss: 2.1920
Epoch 1/2... Discriminator Loss: 0.8670... Generator Loss: 1.6373
Epoch 1/2... Discriminator Loss: 0.9381... Generator Loss: 0.7362
Epoch 1/2... Discriminator Loss: 0.8210... Generator Loss: 1.3953
Epoch 1/2... Discriminator Loss: 1.2294... Generator Loss: 2.6750
Epoch 1/2... Discriminator Loss: 1.3830... Generator Loss: 0.5326
Epoch 1/2... Discriminator Loss: 0.4030... Generator Loss: 1.7258
Epoch 1/2... Discriminator Loss: 0.7528... Generator Loss: 0.9060
Epoch 1/2... Discriminator Loss: 1.1579... Generator Loss: 0.5659
Epoch 1/2... Discriminator Loss: 1.2196... Generator Loss: 2.2079
Epoch 1/2... Discriminator Loss: 1.6576... Generator Loss: 0.3337
Epoch 1/2... Discriminator Loss: 1.3651... Generator Loss: 0.4412
Epoch 1/2... Discriminator Loss: 1.0247... Generator Loss: 2.5581
Epoch 1/2... Discriminator Loss: 0.9043... Generator Loss: 1.5294
Epoch 1/2... Discriminator Loss: 0.9935... Generator Loss: 1.9149
Epoch 1/2... Discriminator Loss: 0.6252... Generator Loss: 1.3567
Epoch 1/2... Discriminator Loss: 0.5638... Generator Loss: 3.5300
Epoch 1/2... Discriminator Loss: 0.7792... Generator Loss: 1.3340
Epoch 1/2... Discriminator Loss: 0.5036... Generator Loss: 2.7151
Epoch 1/2... Discriminator Loss: 5.6961... Generator Loss: 5.3755
Epoch 1/2... Discriminator Loss: 0.9957... Generator Loss: 0.9237
Epoch 1/2... Discriminator Loss: 1.0584... Generator Loss: 0.6259
Epoch 1/2... Discriminator Loss: 1.6855... Generator Loss: 0.3598
Epoch 1/2... Discriminator Loss: 1.0201... Generator Loss: 0.6994
Epoch 1/2... Discriminator Loss: 1.6370... Generator Loss: 0.3059
Epoch 1/2... Discriminator Loss: 0.6810... Generator Loss: 1.5521
Epoch 1/2... Discriminator Loss: 1.1017... Generator Loss: 0.5600
Epoch 1/2... Discriminator Loss: 1.8453... Generator Loss: 0.2608
Epoch 1/2... Discriminator Loss: 0.5876... Generator Loss: 1.0818
Epoch 1/2... Discriminator Loss: 2.3696... Generator Loss: 0.1641
Epoch 1/2... Discriminator Loss: 1.2261... Generator Loss: 0.5453
Epoch 1/2... Discriminator Loss: 0.6368... Generator Loss: 1.0821
Epoch 1/2... Discriminator Loss: 2.5824... Generator Loss: 0.1240
Epoch 1/2... Discriminator Loss: 1.3229... Generator Loss: 0.4859
Epoch 1/2... Discriminator Loss: 1.0460... Generator Loss: 0.6011

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [17]:
batch_size = 64
z_dim = 100
learning_rate = 0.0004
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 0/1... Discriminator Loss: 0.6218... Generator Loss: 6.7542
Epoch 0/1... Discriminator Loss: 0.2210... Generator Loss: 12.0185
Epoch 0/1... Discriminator Loss: 0.0837... Generator Loss: 13.6359
Epoch 0/1... Discriminator Loss: 0.1474... Generator Loss: 2.6577
Epoch 0/1... Discriminator Loss: 0.0799... Generator Loss: 9.0542
Epoch 0/1... Discriminator Loss: 0.9694... Generator Loss: 0.5213
Epoch 0/1... Discriminator Loss: 1.7996... Generator Loss: 0.2328
Epoch 0/1... Discriminator Loss: 0.0940... Generator Loss: 4.6129
Epoch 0/1... Discriminator Loss: 0.5094... Generator Loss: 10.7036
Epoch 0/1... Discriminator Loss: 0.6884... Generator Loss: 1.5429
Epoch 0/1... Discriminator Loss: 0.3922... Generator Loss: 6.2053
Epoch 0/1... Discriminator Loss: 0.6844... Generator Loss: 1.8742
Epoch 0/1... Discriminator Loss: 0.2767... Generator Loss: 5.0851
Epoch 0/1... Discriminator Loss: 0.2680... Generator Loss: 2.0566
Epoch 0/1... Discriminator Loss: 0.2183... Generator Loss: 3.0680
Epoch 0/1... Discriminator Loss: 1.3807... Generator Loss: 8.8281
Epoch 0/1... Discriminator Loss: 0.6517... Generator Loss: 1.0631
Epoch 0/1... Discriminator Loss: 0.6843... Generator Loss: 0.9577
Epoch 0/1... Discriminator Loss: 0.6783... Generator Loss: 3.8748
Epoch 0/1... Discriminator Loss: 0.3414... Generator Loss: 2.8515
Epoch 0/1... Discriminator Loss: 0.3575... Generator Loss: 2.2995
Epoch 0/1... Discriminator Loss: 1.2567... Generator Loss: 0.5501
Epoch 0/1... Discriminator Loss: 0.8016... Generator Loss: 5.2044
Epoch 0/1... Discriminator Loss: 0.8977... Generator Loss: 2.9335
Epoch 0/1... Discriminator Loss: 0.5397... Generator Loss: 1.6186
Epoch 0/1... Discriminator Loss: 0.9090... Generator Loss: 1.8203
Epoch 0/1... Discriminator Loss: 0.8011... Generator Loss: 2.2234
Epoch 0/1... Discriminator Loss: 0.7014... Generator Loss: 1.4191
Epoch 0/1... Discriminator Loss: 1.0575... Generator Loss: 1.1094
Epoch 0/1... Discriminator Loss: 0.9031... Generator Loss: 1.3718
Epoch 0/1... Discriminator Loss: 1.5967... Generator Loss: 0.9122
Epoch 0/1... Discriminator Loss: 0.9074... Generator Loss: 1.2662
Epoch 0/1... Discriminator Loss: 1.1536... Generator Loss: 0.8921
Epoch 0/1... Discriminator Loss: 2.0748... Generator Loss: 0.2825
Epoch 0/1... Discriminator Loss: 1.0040... Generator Loss: 1.0685
Epoch 0/1... Discriminator Loss: 1.0116... Generator Loss: 0.9163
Epoch 0/1... Discriminator Loss: 0.7967... Generator Loss: 1.0896
Epoch 0/1... Discriminator Loss: 0.9013... Generator Loss: 1.3306
Epoch 0/1... Discriminator Loss: 0.9746... Generator Loss: 1.2997
Epoch 0/1... Discriminator Loss: 0.9752... Generator Loss: 1.5311
Epoch 0/1... Discriminator Loss: 0.7305... Generator Loss: 1.5061
Epoch 0/1... Discriminator Loss: 0.5503... Generator Loss: 1.3170
Epoch 0/1... Discriminator Loss: 1.0364... Generator Loss: 0.7458
Epoch 0/1... Discriminator Loss: 1.0248... Generator Loss: 0.8532
Epoch 0/1... Discriminator Loss: 1.0175... Generator Loss: 1.0297
Epoch 0/1... Discriminator Loss: 1.2304... Generator Loss: 1.2793
Epoch 0/1... Discriminator Loss: 1.3685... Generator Loss: 1.1291
Epoch 0/1... Discriminator Loss: 0.9097... Generator Loss: 1.2895
Epoch 0/1... Discriminator Loss: 0.9766... Generator Loss: 1.7817
Epoch 0/1... Discriminator Loss: 1.1587... Generator Loss: 0.8739
Epoch 0/1... Discriminator Loss: 0.8369... Generator Loss: 1.7000
Epoch 0/1... Discriminator Loss: 1.3175... Generator Loss: 2.2688
Epoch 0/1... Discriminator Loss: 0.9237... Generator Loss: 0.7978
Epoch 0/1... Discriminator Loss: 0.7502... Generator Loss: 1.2931
Epoch 0/1... Discriminator Loss: 1.1444... Generator Loss: 1.0582
Epoch 0/1... Discriminator Loss: 0.9437... Generator Loss: 1.4002
Epoch 0/1... Discriminator Loss: 0.8985... Generator Loss: 0.6887
Epoch 0/1... Discriminator Loss: 1.1120... Generator Loss: 0.6667
Epoch 0/1... Discriminator Loss: 0.3703... Generator Loss: 1.7196
Epoch 0/1... Discriminator Loss: 4.7015... Generator Loss: 6.9979
Epoch 0/1... Discriminator Loss: 0.8447... Generator Loss: 1.0757
Epoch 0/1... Discriminator Loss: 0.8757... Generator Loss: 1.5791
Epoch 0/1... Discriminator Loss: 0.9750... Generator Loss: 0.9356
Epoch 0/1... Discriminator Loss: 0.9773... Generator Loss: 0.7636
Epoch 0/1... Discriminator Loss: 1.1661... Generator Loss: 2.4567
Epoch 0/1... Discriminator Loss: 0.7715... Generator Loss: 1.8850
Epoch 0/1... Discriminator Loss: 1.4794... Generator Loss: 0.3984
Epoch 0/1... Discriminator Loss: 1.4443... Generator Loss: 0.5665
Epoch 0/1... Discriminator Loss: 0.9071... Generator Loss: 2.1894
Epoch 0/1... Discriminator Loss: 1.1729... Generator Loss: 1.5581
Epoch 0/1... Discriminator Loss: 0.8425... Generator Loss: 1.9790
Epoch 0/1... Discriminator Loss: 1.2685... Generator Loss: 1.8635
Epoch 0/1... Discriminator Loss: 1.3507... Generator Loss: 2.4140
Epoch 0/1... Discriminator Loss: 0.6444... Generator Loss: 1.5539
Epoch 0/1... Discriminator Loss: 1.2362... Generator Loss: 0.8157
Epoch 0/1... Discriminator Loss: 0.9081... Generator Loss: 0.9835
Epoch 0/1... Discriminator Loss: 1.1440... Generator Loss: 0.7494
Epoch 0/1... Discriminator Loss: 0.8863... Generator Loss: 1.1121
Epoch 0/1... Discriminator Loss: 0.7551... Generator Loss: 1.5537
Epoch 0/1... Discriminator Loss: 0.8504... Generator Loss: 1.0750
Epoch 0/1... Discriminator Loss: 1.2803... Generator Loss: 0.5488
Epoch 0/1... Discriminator Loss: 0.7217... Generator Loss: 1.2239
Epoch 0/1... Discriminator Loss: 0.9225... Generator Loss: 1.5334
Epoch 0/1... Discriminator Loss: 1.0059... Generator Loss: 1.1359
Epoch 0/1... Discriminator Loss: 0.9815... Generator Loss: 0.8485
Epoch 0/1... Discriminator Loss: 0.8469... Generator Loss: 0.9791
Epoch 0/1... Discriminator Loss: 0.8377... Generator Loss: 1.0540
Epoch 0/1... Discriminator Loss: 0.8266... Generator Loss: 1.0712
Epoch 0/1... Discriminator Loss: 0.7027... Generator Loss: 1.4701
Epoch 0/1... Discriminator Loss: 1.7246... Generator Loss: 0.3407
Epoch 0/1... Discriminator Loss: 0.7190... Generator Loss: 1.5031
Epoch 0/1... Discriminator Loss: 0.9852... Generator Loss: 1.6414
Epoch 0/1... Discriminator Loss: 0.9154... Generator Loss: 1.0538
Epoch 0/1... Discriminator Loss: 0.9514... Generator Loss: 0.9557
Epoch 0/1... Discriminator Loss: 0.7041... Generator Loss: 1.3981
Epoch 0/1... Discriminator Loss: 0.8933... Generator Loss: 0.8650
Epoch 0/1... Discriminator Loss: 1.0489... Generator Loss: 1.8639
Epoch 0/1... Discriminator Loss: 0.7536... Generator Loss: 1.2305
Epoch 0/1... Discriminator Loss: 1.0261... Generator Loss: 2.3858
Epoch 0/1... Discriminator Loss: 1.0361... Generator Loss: 0.8966
Epoch 0/1... Discriminator Loss: 1.1574... Generator Loss: 0.5820
Epoch 0/1... Discriminator Loss: 1.1869... Generator Loss: 1.0538
Epoch 0/1... Discriminator Loss: 0.8347... Generator Loss: 1.7143
Epoch 0/1... Discriminator Loss: 0.9689... Generator Loss: 1.3656
Epoch 0/1... Discriminator Loss: 0.8124... Generator Loss: 1.1253
Epoch 0/1... Discriminator Loss: 0.9841... Generator Loss: 0.9885
Epoch 0/1... Discriminator Loss: 0.7878... Generator Loss: 1.0688
Epoch 0/1... Discriminator Loss: 1.1783... Generator Loss: 0.6539
Epoch 0/1... Discriminator Loss: 1.1736... Generator Loss: 0.8135
Epoch 0/1... Discriminator Loss: 0.6119... Generator Loss: 1.7221
Epoch 0/1... Discriminator Loss: 1.0845... Generator Loss: 0.7065
Epoch 0/1... Discriminator Loss: 0.9617... Generator Loss: 1.2406
Epoch 0/1... Discriminator Loss: 0.9032... Generator Loss: 1.5311
Epoch 0/1... Discriminator Loss: 0.9991... Generator Loss: 0.8057
Epoch 0/1... Discriminator Loss: 1.0544... Generator Loss: 1.6917
Epoch 0/1... Discriminator Loss: 0.8874... Generator Loss: 1.1187
Epoch 0/1... Discriminator Loss: 0.9024... Generator Loss: 1.3797
Epoch 0/1... Discriminator Loss: 1.2018... Generator Loss: 1.8046
Epoch 0/1... Discriminator Loss: 0.8676... Generator Loss: 1.5848
Epoch 0/1... Discriminator Loss: 1.0200... Generator Loss: 0.8683
Epoch 0/1... Discriminator Loss: 1.2632... Generator Loss: 0.8363
Epoch 0/1... Discriminator Loss: 0.8942... Generator Loss: 0.9055
Epoch 0/1... Discriminator Loss: 0.9545... Generator Loss: 0.8812
Epoch 0/1... Discriminator Loss: 1.0817... Generator Loss: 0.7342
Epoch 0/1... Discriminator Loss: 1.0601... Generator Loss: 0.7125
Epoch 0/1... Discriminator Loss: 0.9638... Generator Loss: 1.2588
Epoch 0/1... Discriminator Loss: 1.3802... Generator Loss: 0.4417
Epoch 0/1... Discriminator Loss: 0.7872... Generator Loss: 1.4050
Epoch 0/1... Discriminator Loss: 0.8713... Generator Loss: 1.1966
Epoch 0/1... Discriminator Loss: 1.4266... Generator Loss: 0.3936
Epoch 0/1... Discriminator Loss: 1.0380... Generator Loss: 0.9849
Epoch 0/1... Discriminator Loss: 0.8466... Generator Loss: 1.2534
Epoch 0/1... Discriminator Loss: 0.9786... Generator Loss: 0.8953
Epoch 0/1... Discriminator Loss: 0.7977... Generator Loss: 1.3430
Epoch 0/1... Discriminator Loss: 1.2394... Generator Loss: 0.5482
Epoch 0/1... Discriminator Loss: 1.0871... Generator Loss: 0.9110
Epoch 0/1... Discriminator Loss: 1.0683... Generator Loss: 1.6584
Epoch 0/1... Discriminator Loss: 0.9218... Generator Loss: 1.8546
Epoch 0/1... Discriminator Loss: 1.4405... Generator Loss: 0.4413
Epoch 0/1... Discriminator Loss: 1.0229... Generator Loss: 1.2440
Epoch 0/1... Discriminator Loss: 1.0535... Generator Loss: 0.9958
Epoch 0/1... Discriminator Loss: 0.9219... Generator Loss: 1.4968
Epoch 0/1... Discriminator Loss: 1.2502... Generator Loss: 0.5496
Epoch 0/1... Discriminator Loss: 0.9177... Generator Loss: 1.5827
Epoch 0/1... Discriminator Loss: 0.9988... Generator Loss: 1.9179
Epoch 0/1... Discriminator Loss: 1.0646... Generator Loss: 1.8802
Epoch 0/1... Discriminator Loss: 1.1169... Generator Loss: 1.0421
Epoch 0/1... Discriminator Loss: 0.8214... Generator Loss: 1.0580
Epoch 0/1... Discriminator Loss: 0.8968... Generator Loss: 1.2949
Epoch 0/1... Discriminator Loss: 0.9167... Generator Loss: 1.3115
Epoch 0/1... Discriminator Loss: 0.9070... Generator Loss: 1.5452
Epoch 0/1... Discriminator Loss: 0.9268... Generator Loss: 0.9459
Epoch 0/1... Discriminator Loss: 0.7334... Generator Loss: 1.0918
Epoch 0/1... Discriminator Loss: 0.8648... Generator Loss: 1.3180
Epoch 0/1... Discriminator Loss: 0.9087... Generator Loss: 1.2271
Epoch 0/1... Discriminator Loss: 0.8439... Generator Loss: 1.0740
Epoch 0/1... Discriminator Loss: 0.8228... Generator Loss: 1.3891
Epoch 0/1... Discriminator Loss: 0.8584... Generator Loss: 1.4643
Epoch 0/1... Discriminator Loss: 0.9666... Generator Loss: 0.8324
Epoch 0/1... Discriminator Loss: 1.3077... Generator Loss: 0.5482
Epoch 0/1... Discriminator Loss: 1.1276... Generator Loss: 0.6679
Epoch 0/1... Discriminator Loss: 0.8595... Generator Loss: 1.0244
Epoch 0/1... Discriminator Loss: 0.9293... Generator Loss: 1.4445
Epoch 0/1... Discriminator Loss: 0.9492... Generator Loss: 1.1703
Epoch 0/1... Discriminator Loss: 1.0612... Generator Loss: 0.6851
Epoch 0/1... Discriminator Loss: 0.9470... Generator Loss: 1.5096
Epoch 0/1... Discriminator Loss: 0.9478... Generator Loss: 1.5059
Epoch 0/1... Discriminator Loss: 0.9809... Generator Loss: 1.4573
Epoch 0/1... Discriminator Loss: 0.8228... Generator Loss: 1.0141
Epoch 0/1... Discriminator Loss: 1.0726... Generator Loss: 1.7431
Epoch 0/1... Discriminator Loss: 1.0359... Generator Loss: 0.8059
Epoch 0/1... Discriminator Loss: 1.0275... Generator Loss: 0.9808
Epoch 0/1... Discriminator Loss: 1.2132... Generator Loss: 1.2436
Epoch 0/1... Discriminator Loss: 1.3145... Generator Loss: 0.5336
Epoch 0/1... Discriminator Loss: 1.4615... Generator Loss: 0.4201
Epoch 0/1... Discriminator Loss: 0.8030... Generator Loss: 1.3903
Epoch 0/1... Discriminator Loss: 1.0982... Generator Loss: 0.7073
Epoch 0/1... Discriminator Loss: 0.8838... Generator Loss: 1.1827
Epoch 0/1... Discriminator Loss: 1.0726... Generator Loss: 0.6807
Epoch 0/1... Discriminator Loss: 0.9571... Generator Loss: 0.8725
Epoch 0/1... Discriminator Loss: 1.0088... Generator Loss: 0.9799
Epoch 0/1... Discriminator Loss: 1.0628... Generator Loss: 1.8003
Epoch 0/1... Discriminator Loss: 1.0570... Generator Loss: 0.7860
Epoch 0/1... Discriminator Loss: 1.1958... Generator Loss: 1.0913
Epoch 0/1... Discriminator Loss: 1.2007... Generator Loss: 1.1785
Epoch 0/1... Discriminator Loss: 1.1588... Generator Loss: 0.7312
Epoch 0/1... Discriminator Loss: 1.2716... Generator Loss: 1.8042
Epoch 0/1... Discriminator Loss: 0.9001... Generator Loss: 0.9568
Epoch 0/1... Discriminator Loss: 1.0648... Generator Loss: 0.5978
Epoch 0/1... Discriminator Loss: 1.1831... Generator Loss: 0.6758
Epoch 0/1... Discriminator Loss: 1.1347... Generator Loss: 1.0278
Epoch 0/1... Discriminator Loss: 1.2148... Generator Loss: 1.0529
Epoch 0/1... Discriminator Loss: 0.9657... Generator Loss: 1.3807
Epoch 0/1... Discriminator Loss: 1.3535... Generator Loss: 0.4648
Epoch 0/1... Discriminator Loss: 1.3116... Generator Loss: 0.4868
Epoch 0/1... Discriminator Loss: 1.2207... Generator Loss: 0.6048
Epoch 0/1... Discriminator Loss: 1.1886... Generator Loss: 1.5511
Epoch 0/1... Discriminator Loss: 1.0087... Generator Loss: 1.3831
Epoch 0/1... Discriminator Loss: 1.0957... Generator Loss: 0.8323
Epoch 0/1... Discriminator Loss: 0.8210... Generator Loss: 1.1690
Epoch 0/1... Discriminator Loss: 1.1312... Generator Loss: 0.8121
Epoch 0/1... Discriminator Loss: 1.0869... Generator Loss: 0.7852
Epoch 0/1... Discriminator Loss: 1.0890... Generator Loss: 1.8055
Epoch 0/1... Discriminator Loss: 0.9932... Generator Loss: 0.9091
Epoch 0/1... Discriminator Loss: 1.0339... Generator Loss: 1.0753
Epoch 0/1... Discriminator Loss: 0.7161... Generator Loss: 1.3734
Epoch 0/1... Discriminator Loss: 0.9643... Generator Loss: 0.8843
Epoch 0/1... Discriminator Loss: 1.1543... Generator Loss: 0.7500
Epoch 0/1... Discriminator Loss: 0.9989... Generator Loss: 0.9388
Epoch 0/1... Discriminator Loss: 0.9737... Generator Loss: 0.8704
Epoch 0/1... Discriminator Loss: 0.7774... Generator Loss: 1.2429
Epoch 0/1... Discriminator Loss: 0.9960... Generator Loss: 0.8373
Epoch 0/1... Discriminator Loss: 0.8747... Generator Loss: 1.1697
Epoch 0/1... Discriminator Loss: 1.4014... Generator Loss: 0.4005
Epoch 0/1... Discriminator Loss: 0.9469... Generator Loss: 1.0860
Epoch 0/1... Discriminator Loss: 1.1476... Generator Loss: 0.6653
Epoch 0/1... Discriminator Loss: 0.8059... Generator Loss: 1.3371
Epoch 0/1... Discriminator Loss: 0.9960... Generator Loss: 1.0451
Epoch 0/1... Discriminator Loss: 1.6040... Generator Loss: 2.4551
Epoch 0/1... Discriminator Loss: 1.1574... Generator Loss: 0.8711
Epoch 0/1... Discriminator Loss: 0.8172... Generator Loss: 1.1679
Epoch 0/1... Discriminator Loss: 0.8709... Generator Loss: 1.4337
Epoch 0/1... Discriminator Loss: 0.9006... Generator Loss: 1.0631
Epoch 0/1... Discriminator Loss: 0.9492... Generator Loss: 1.7496
Epoch 0/1... Discriminator Loss: 0.8255... Generator Loss: 1.0830
Epoch 0/1... Discriminator Loss: 0.7952... Generator Loss: 1.3048
Epoch 0/1... Discriminator Loss: 0.9118... Generator Loss: 1.0866
Epoch 0/1... Discriminator Loss: 0.8301... Generator Loss: 1.4702
Epoch 0/1... Discriminator Loss: 0.7691... Generator Loss: 1.4263
Epoch 0/1... Discriminator Loss: 1.0813... Generator Loss: 1.0316
Epoch 0/1... Discriminator Loss: 1.3005... Generator Loss: 0.4534
Epoch 0/1... Discriminator Loss: 0.9630... Generator Loss: 0.8268
Epoch 0/1... Discriminator Loss: 1.1852... Generator Loss: 0.5974
Epoch 0/1... Discriminator Loss: 1.2266... Generator Loss: 0.5750
Epoch 0/1... Discriminator Loss: 0.9113... Generator Loss: 1.2864
Epoch 0/1... Discriminator Loss: 0.9877... Generator Loss: 1.0237
Epoch 0/1... Discriminator Loss: 0.9461... Generator Loss: 1.0671
Epoch 0/1... Discriminator Loss: 1.3689... Generator Loss: 0.4766
Epoch 0/1... Discriminator Loss: 1.4372... Generator Loss: 0.4686
Epoch 0/1... Discriminator Loss: 0.7608... Generator Loss: 1.3928
Epoch 0/1... Discriminator Loss: 0.8381... Generator Loss: 0.8828
Epoch 0/1... Discriminator Loss: 1.3588... Generator Loss: 0.4760
Epoch 0/1... Discriminator Loss: 1.1854... Generator Loss: 0.5652
Epoch 0/1... Discriminator Loss: 1.5202... Generator Loss: 0.3584
Epoch 0/1... Discriminator Loss: 0.9659... Generator Loss: 0.9307
Epoch 0/1... Discriminator Loss: 1.0385... Generator Loss: 0.7233
Epoch 0/1... Discriminator Loss: 0.8080... Generator Loss: 1.1431
Epoch 0/1... Discriminator Loss: 1.0286... Generator Loss: 0.8010
Epoch 0/1... Discriminator Loss: 0.9443... Generator Loss: 1.6773
Epoch 0/1... Discriminator Loss: 0.9626... Generator Loss: 1.3939
Epoch 0/1... Discriminator Loss: 1.0114... Generator Loss: 1.4170
Epoch 0/1... Discriminator Loss: 0.7668... Generator Loss: 1.5315
Epoch 0/1... Discriminator Loss: 1.0818... Generator Loss: 1.6709
Epoch 0/1... Discriminator Loss: 0.8006... Generator Loss: 1.0052
Epoch 0/1... Discriminator Loss: 0.9841... Generator Loss: 0.9128
Epoch 0/1... Discriminator Loss: 1.2096... Generator Loss: 2.0731
Epoch 0/1... Discriminator Loss: 0.9470... Generator Loss: 0.9893
Epoch 0/1... Discriminator Loss: 0.9071... Generator Loss: 0.9222
Epoch 0/1... Discriminator Loss: 0.8249... Generator Loss: 2.0389
Epoch 0/1... Discriminator Loss: 0.8644... Generator Loss: 1.0584
Epoch 0/1... Discriminator Loss: 0.9552... Generator Loss: 1.2485
Epoch 0/1... Discriminator Loss: 1.0596... Generator Loss: 1.1370
Epoch 0/1... Discriminator Loss: 1.2388... Generator Loss: 1.7862
Epoch 0/1... Discriminator Loss: 1.0593... Generator Loss: 0.7082
Epoch 0/1... Discriminator Loss: 1.0614... Generator Loss: 0.7431
Epoch 0/1... Discriminator Loss: 0.7083... Generator Loss: 1.8247
Epoch 0/1... Discriminator Loss: 1.1029... Generator Loss: 0.7334
Epoch 0/1... Discriminator Loss: 0.9890... Generator Loss: 1.0843
Epoch 0/1... Discriminator Loss: 1.2608... Generator Loss: 1.5345
Epoch 0/1... Discriminator Loss: 0.9449... Generator Loss: 1.2335
Epoch 0/1... Discriminator Loss: 1.0292... Generator Loss: 0.6908
Epoch 0/1... Discriminator Loss: 0.9660... Generator Loss: 1.2418
Epoch 0/1... Discriminator Loss: 0.6464... Generator Loss: 1.6553
Epoch 0/1... Discriminator Loss: 1.1076... Generator Loss: 1.0662
Epoch 0/1... Discriminator Loss: 0.8868... Generator Loss: 1.0938
Epoch 0/1... Discriminator Loss: 0.9061... Generator Loss: 1.3332
Epoch 0/1... Discriminator Loss: 1.0842... Generator Loss: 1.3332
Epoch 0/1... Discriminator Loss: 1.6581... Generator Loss: 0.3070
Epoch 0/1... Discriminator Loss: 0.9955... Generator Loss: 1.1725
Epoch 0/1... Discriminator Loss: 0.9307... Generator Loss: 1.7220
Epoch 0/1... Discriminator Loss: 0.8856... Generator Loss: 1.3696
Epoch 0/1... Discriminator Loss: 0.8885... Generator Loss: 0.9771
Epoch 0/1... Discriminator Loss: 1.1424... Generator Loss: 0.6938
Epoch 0/1... Discriminator Loss: 1.1151... Generator Loss: 0.6085
Epoch 0/1... Discriminator Loss: 1.2542... Generator Loss: 1.2329
Epoch 0/1... Discriminator Loss: 1.0053... Generator Loss: 0.9531
Epoch 0/1... Discriminator Loss: 1.1311... Generator Loss: 1.8672
Epoch 0/1... Discriminator Loss: 1.2338... Generator Loss: 0.5941
Epoch 0/1... Discriminator Loss: 1.2994... Generator Loss: 0.5540
Epoch 0/1... Discriminator Loss: 1.0659... Generator Loss: 0.6569
Epoch 0/1... Discriminator Loss: 0.9502... Generator Loss: 0.9118
Epoch 0/1... Discriminator Loss: 1.0978... Generator Loss: 1.1263
Epoch 0/1... Discriminator Loss: 1.1933... Generator Loss: 0.6380
Epoch 0/1... Discriminator Loss: 0.9971... Generator Loss: 0.8017
Epoch 0/1... Discriminator Loss: 1.0339... Generator Loss: 0.6719
Epoch 0/1... Discriminator Loss: 1.0155... Generator Loss: 1.9844
Epoch 0/1... Discriminator Loss: 0.9237... Generator Loss: 1.1397
Epoch 0/1... Discriminator Loss: 1.1796... Generator Loss: 0.6371
Epoch 0/1... Discriminator Loss: 0.9489... Generator Loss: 1.3966
Epoch 0/1... Discriminator Loss: 1.0631... Generator Loss: 0.6491
Epoch 0/1... Discriminator Loss: 0.8994... Generator Loss: 1.1534
Epoch 0/1... Discriminator Loss: 1.0675... Generator Loss: 0.6466
Epoch 0/1... Discriminator Loss: 1.2542... Generator Loss: 0.4662
Epoch 0/1... Discriminator Loss: 1.3066... Generator Loss: 1.7855
Epoch 0/1... Discriminator Loss: 0.9254... Generator Loss: 1.0968
Epoch 0/1... Discriminator Loss: 0.9190... Generator Loss: 1.0831
Epoch 0/1... Discriminator Loss: 1.4598... Generator Loss: 1.6319
Epoch 0/1... Discriminator Loss: 0.7803... Generator Loss: 1.4055
Epoch 0/1... Discriminator Loss: 1.2578... Generator Loss: 0.5636
Epoch 0/1... Discriminator Loss: 0.7222... Generator Loss: 1.2950
Epoch 0/1... Discriminator Loss: 1.4321... Generator Loss: 0.5750
Epoch 0/1... Discriminator Loss: 0.9480... Generator Loss: 0.7580
Epoch 0/1... Discriminator Loss: 1.1242... Generator Loss: 0.6416
Epoch 0/1... Discriminator Loss: 1.0508... Generator Loss: 0.9040
Epoch 0/1... Discriminator Loss: 1.2106... Generator Loss: 0.6165
Epoch 0/1... Discriminator Loss: 0.8006... Generator Loss: 1.3873

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.